biosigner: Signature discovery from omics data

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Feature selection is critical in omics data analysis to extract
restricted and meaningful molecular signatures from complex and high-dimension
data, and to build robust classifiers. This package implements a new method to
assess the relevance of the variables for the prediction performances of the
classifier. The approach can be run in parallel with the PLS-DA, Random Forest,
and SVM binary classifiers. The signatures and the corresponding 'restricted'
models are returned, enabling future predictions on new datasets. A Galaxy
implementation of the package is available within the Workflow4metabolomics.org
online infrastructure for computational metabolomics.